The correct use of cryptography is central to ensuring data security in
modern software systems. Hence, several academic and commercial static analysis
tools have been developed for detecting and mitigating crypto-API misuse. While
developers are optimistically adopting these crypto-API misuse detectors (or
crypto-detectors) in their software development cycles, this momentum must be
accompanied by a rigorous understanding of their effectiveness at finding
crypto-API misuse in practice. This paper presents the MASC framework, which
enables a systematic and data-driven evaluation of crypto-detectors using
mutation testing. We ground MASC in a comprehensive view of the problem space
by developing a data-driven taxonomy of existing crypto-API misuse, containing
$105$ misuse cases organized among nine semantic clusters. We develop $12$
generalizable usage-based mutation operators and three mutation scopes that can
expressively instantiate thousands of compilable variants of the misuse cases
for thoroughly evaluating crypto-detectors. Using MASC, we evaluate nine major
crypto-detectors and discover $19$ unique, undocumented flaws that severely
impact the ability of crypto-detectors to discover misuses in practice. We
conclude with a discussion on the diverse perspectives that influence the
design of crypto-detectors and future directions towards building
security-focused crypto-detectors by design.

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